In-network content caching exploiting variation in mobility-prediction accuracy

ABSTRACT

A network device in a network edge receives content directed to a mobile device attached to the network edge from an upstream network device and forwards the content toward the mobile device. The network device makes a decision whether to cache the content based at least in part on a popularity of the content in a region covered by the network device and a prediction error for an estimated probability that the mobile device will transition from the region to another region. The popularity is directly correlated with a first bias toward caching the content. The prediction error is inversely correlated with a second bias toward caching the content. The decision is implemented: the network device either caches the content or foregoes caching the content, in accordance with the decision.

TECHNICAL FIELD

The present disclosure generally relates to caching content in anetwork, and in particular, to deciding whether to cache content basedat least in part on a prediction error for estimated mobility of adevice requesting the content.

BACKGROUND

Distribution of content from the cloud to mobile devices is increasinglymore prevalent. For example, over-the-air (OTA) software updates areprovided to vehicles, media is provided to user devices in vehicles, andmobile Internet-of-Things (IoT) applications consume increasing amountsof content. Distributing such content with low latency to a large numberof mobile devices, some of which may have complicated mobility patternsor no pattern to their movement, presents significant challenges.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the present disclosure can be understood by those of ordinaryskill in the art, a more detailed description may be had by reference toaspects of some illustrative implementations, some of which are shown inthe accompanying drawings.

FIG. 1 is a block diagram illustrating a network architecture inaccordance with some implementations.

FIG. 2 shows a flowchart illustrating a networking method in accordancewith some implementations.

FIG. 3 is a block diagram of a network device in accordance with someimplementations.

In accordance with common practice the various features illustrated inthe drawings may not be drawn to scale. Accordingly, the dimensions ofthe various features may be arbitrarily expanded or reduced for clarity.In addition, some of the drawings may not depict all of the componentsof a given system, method or device. Finally, like reference numeralsmay be used to denote like features throughout the specification andfigures.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Numerous details are described in order to provide a thoroughunderstanding of the example implementations shown in the drawings.However, the drawings merely show some example aspects of the presentdisclosure and are therefore not to be considered limiting. Those ofordinary skill in the art will appreciate that other effective aspectsand/or variants do not include all of the specific details describedherein. Moreover, well-known systems, methods, components, devices andcircuits have not been described in exhaustive detail so as not toobscure more pertinent aspects of the example implementations describedherein.

Overview

Various implementations disclosed herein enable a network device in anetwork edge intelligently to decide whether to cache content for mobiledevices, to allow the content to be delivered with low latency whilemaking judicious use of available memory. For example, a networkingmethod is performed by a network device in a network edge. The networkdevice includes one or more processors and memory (e.g., non-transitorymemory) storing instructions for execution by the one or moreprocessors. In the method, content directed to a mobile device attachedto the network edge is received from an upstream network device and isforwarded toward the mobile device. A decision is made whether to cachethe content at the network device based at least in part on a popularityof the content in a region covered by the network device and aprediction error for an estimated probability that the mobile devicewill transition from the region to another region. The popularity isdirectly correlated with a first bias toward caching the content. Theprediction error is inversely correlated with a second bias towardcaching the content. The decision is implemented: the network deviceeither caches the content or foregoes caching the content, in accordancewith the decision.

FIG. 1 is a block diagram illustrating a network architecture 100 inaccordance with some implementations. The network architecture 100includes a three-tiered network edge. The first tier includes aplurality of access points to which mobile devices attach. In theexample of FIG. 1, the access points are road-side units (RSUs) 114-1through 114-5 and the mobile devices are vehicles (or electronic devicesin vehicles) 118-1 through 118-5, although the access points and mobiledevices are not so limited. The second tier includes a plurality ofgateways (GWs) 110 upstream from respective RSUs 114. A first gateway110-1 is upstream from, and communicates with, RSUs 114-1 through 114-3.A second gateway 110-2 is upstream from, and communicates with, RSUs114-4 and 114-5. The third tier includes a server 106 that is upstreamfrom, and communicates with, the gateways 110-1 and 110-2. In theexample of FIG. 1, the server 106 is a mobile-edge-computing (MEC)server, although the server 106 is not so limited.

Each of the RSUs 114 provides wireless network access to, and thusserves as an access point for, vehicles 118 (or, more generally,respective mobile devices) in respective regions 120. The RSU 114-1provides wireless access to a vehicle 118-1 in a region 120-1, the RSU114-3 provides wireless access to a vehicle 118-4 in a region 120-3, andso on. A vehicle 118 (or, more generally, a mobile device) is said to beattached to the RSU 114 that it uses for wireless network access. Avehicle 118 may attach to an RSU 114 by forming a wireless connectionwith the RSU 114 (e.g., upon entering a corresponding region 120, orafter being turned on within a region 120). A vehicle 118 may transitionfrom a first region 120 to a second region 120; the vehicle 118 dropsits attachment to a corresponding first RSU 114 and attaches to a secondRSU 114 accordingly. In the example of FIG. 1, the vehicle 118-3 istransitioning from the first region 120-1 to the second region 120-2.The vehicle 118-3 will thus drop its attachment to the RSU 114-1 andwill attach to the RSU 114-2.

All or a portion of the network devices in the network edge includememory for caching content. (The term “caching” as used herein refers tostoring content locally at a particular device and does not imply thatthe content is stored in any particular type of memory.) In someimplementations, the network devices implement information-centricnetworking (ICN) or hybrid ICN, and content requests from mobile devicesare ICN/hICN interests. In the example of FIG. 1, the RSUs 114 includerespective memories 116, the gateways 110 include respective memories112, and the server 106 includes memory 108. If a network devicereceives a request, originating from a vehicle 118, for content that islocally cached, the network device can respond to the request bytransmitting the requested content downstream toward the requestingvehicle 118, without passing the request further upstream. For example,the vehicle 118-1 transmits a request for content to the RSU 114-1. Ifthe RSU 114-1 has the requested content cached in its memory 116, itresponds by transmitting the content to the vehicle 118-1. Otherwise,the RSU 114-1 forwards the request upstream to the gateway 110-1. If thegateway 110-1 has the requested content cached in its memory 116, itresponds by transmitting the content downstream to the RSU 114-1, andthus toward the vehicle 118-1. Otherwise, the gateway 110-1 forwards therequest upstream to the server 106. If the server 106 1 has therequested content cached in its memory 108, it responds by transmittingthe content downstream to the gateway 110-1, and thus toward the vehicle118-1. Otherwise, the server 106 forwards the request through one ormore networks 104 to a remote device (e.g., server) with the content(e.g., the server 106 goes out to the cloud to obtain the content).Caching content in the memories 116, 112, and/or 108 results in lowlatency for responding to content requests and also reduces networktraffic by limiting the path taken by content requests. However, becausethe size of the memories 116, 112, and 108 is of course limited,decisions regarding whether to cache particular content items should bemade intelligently.

In some implementations, a traffic controller 102 monitors the mobiledevices and predicts future movement of the mobile devices (e.g., usinga Markovian predictor with a fixed history length H). The trafficcontroller 102 estimates the probabilities that a mobile device willtransition from a given region 120 to other regions 120, and thus fromattachment to a given RSU 114 to other RSUs 114. The traffic controller120 also calculates prediction errors for respective probabilities. Forexample, for a particular mobile device (e.g., vehicle 118), the trafficcontroller generates a set of transition probabilities in an array Pwith a prediction error E. In some implementations, the prediction erroris based at least in part on the presence or absence of a historicalpattern of movement for the mobile device. For example, a vehicle 118may have a low prediction error on weekdays, when it follows the samecommute, but may have a high prediction error on weekends, when it doesnot follow a set pattern. The traffic controller 102 may estimate thesetransition probabilities and calculate these errors for all mobiledevices (e.g., all vehicles 118) at the network edge.

The traffic controller 102 provides the transition probabilities anderrors to the server 106, gateways 110, and/or RSUs 114 of the networkedge. In some implementations, the traffic controller 102 iscommunicatively coupled to the network edge through one or more networks104 (e.g., the Internet, other wide-area networks (WANs),metropolitan-area networks (MAN), etc.), and transmits the transitionprobabilities and errors to the network devices of the network edgethrough the one or more networks 104. The traffic controller 102 thusmay be situated outside of the network edge (e.g., implemented in thecloud). Alternatively, the traffic controller 102 may be instantiated ina network device in the network edge (e.g., in the server 106).

FIG. 2 shows a flowchart illustrating a networking method 200 inaccordance with some implementations. The method 200 is performed (202)by a network device in the network edge (e.g., an RSU 114 or otheraccess point, a gateway 110, or the server 106). In someimplementations, respective instances of the method 200 are performedrepeatedly by all or a portion of the network devices in the networkedge.

In the method 200, content directed to a mobile device attached to thenetwork edge is received (204) from an upstream network device. In someimplementations, the mobile device is an electronic device in a vehicle118 (e.g., a mobile device that is part of the vehicle 118 or that is inthe vehicle 118). The content is forwarded (206) toward the mobiledevice. The content may be received (204) in response to a request forthe content that the network device (or another network device)previously received from the mobile device and forwarded to the upstreamdevice (or another upstream device).

In some implementations, an estimated probability that the mobile devicewill transition between regions and/or a corresponding prediction errorare received (208) from the traffic controller 102, as discussed above.

In some implementations, the popularity of the content in a regioncovered by the network device is measured (210). If the network deviceis an RSU 114 or other access point, this region is the correspondingregion 120. If the network device is a gateway 110 or server 106, thisregion is the set of regions 120 for all downstream access points. Tomeasure the popularity, the network device may count requests for thecontent received by the network device (e.g., requests originating frommobile devices) and/or instances of the content received by the networkdevice (e.g., instances of the content being transmitted downstream inresponse to requests from mobile devices). The network device may usecounters 316 (FIG. 3) to keep these counts. The network device mayperform sampling to reduce the amount of data to be stored to determinethe popularity. For example, the network device samples content-itemrequests received at the network device and counts sampled content-itemrequests that are requests for the content. Alternatively, or inaddition, the network device samples content items received at thenetwork device and counts sampled content items that are instances ofthe content.

A decision is made (212) whether to cache the content at the networkdevice based at least in part on (i) the popularity of the content inthe region covered by the network device and (ii) the prediction errorfor the estimated probability. The popularity is directly correlatedwith a first bias toward caching the content: the higher the popularity,the more likely the network device is to cache the content. Theprediction error is inversely correlated with a second bias towardcaching the content: the lower the prediction error, the more likely thenetwork device is to cache the content.

The second bias may be a function of the tier of the network edge inwhich the network device performing the method 200 is situated. Forexample, if the network device is an access point (e.g., RSU 114) towhich the mobile device is attached, then the second bias has amagnitude such that, for a given prediction error, the network device isless likely to decide to cache the content than is a respective gateway110 upstream of the access point. If the network device is a gateway 110upstream of the access point (e.g., RSU 114) to which the mobile deviceis attached, then the second bias has a magnitude such that, for a givenprediction error, the network device is more likely to decide to cachethe content than is the downstream access point. Everything else beingequal, a gateway 110 is thus more likely to cache content for a mobiledevice with a high prediction error than is an access point, inaccordance with some implementations. That is, a gateway 110 is moretolerant of prediction error than an access point in deciding to cachecontent, because a mobile device with high prediction error is morelikely to stay within the set of regions 120 covered by the gateway 110than within the single region 120 covered by the access point. Contentcached at the gateway 110 is thus more likely to be re-used than contentcached at the access point. While the access point offers lower latencythan the gateway 110, a high prediction error suggests that caching thecontent at the access point may be wasteful of memory.

Similarly, if the network device is a gateway 110 upstream from anaccess point (e.g., RSU 114) to which the mobile device is attached,then the second bias has a magnitude such that, for a given predictionerror, the network device is less likely to decide to cache the contentthan is the upstream server 106. If the network device is the server106, which is upstream of the gateway 110 for the access point to whichthe mobile device is attached, then the second bias has a magnitude suchthat, for a given prediction error, the network device is more likely todecide to cache the content than is the downstream gateway 110.Everything else being equal, the server 106 is thus more likely to cachecontent for a mobile device with a high prediction error than is agateway 110, in accordance with some implementations.

In some implementations, the decision is further based (214) at least inpart on a QoS level of the content. The QoS level is directly correlatedwith a third bias toward caching the content. A higher QoS levelindicates a higher desired or guaranteed quality of service. Cachingcontent helps to ensure that the quality of service is met. The biastoward caching for high QoS levels reflects this fact and helps toensure low latency for providing content with a high QoS. In oneexample, OTA updates to vehicular software are assigned a high QoS toensure that the updates are rolled out promptly. The high QoS for theseupdates may result from safety issues that the updates address or from apremium paid by vehicle owners.

In some implementations, the decision is not based (216) on whetherother network devices in the network edge have cached the content. Forexample, the network device does not receive communications indicatingwhether the other network devices in the network edge have cached thecontent. Various network devices in the network edge thus decide whetherto cache content independently of each other. Network traffic istherefore reduced, because network devices do not message each otherregarding whether or not they have cached particular content.

In some implementations, to make the decision, a utility function iscalculated that accounts for the first bias, the second bias, and anyother biases relevant to the decision (e.g., the third bias relating toQoS). A determination is made as to whether the utility functionsatisfies (e.g., exceeds, or equals or exceeds) a threshold. If thethreshold is satisfied, the decision is to cache the content. If thethreshold is not satisfied, the decision is to forego caching thecontent.

One example of a utility function that may be used to make the decisionis:U(x)=Σ_(i=1) ^(n) U _(i)(x _(i))  (1)where n is the total number of biases (i.e., factors) relevant to thedecision, i indexes the biases, x_(i) is a suitably weighted (e.g.,normalized) value of the bias i, and U_(i) (x_(i)) is a function thatindicates the degree of utility for x_(i). For example,

$\begin{matrix}{{U_{i}(x)} = \frac{x^{\Lambda - \alpha}}{\Lambda - \alpha}} & (2)\end{matrix}$where Λ and α are constants, and α≥1. In another example,U _(i)(x)=log x  (3).

The decision is implemented: the network device either caches (220) thecontent or foregoes caching (218) the content, in accordance with thedecision. In some implementations, cached content is stored in thememory 116, 112, or 108 of the network device. If the memory spaceallocated for caching content is full, then content that was previouslycached at the network device is evicted to make room for caching the newcontent. In some implementations, a least-recently-used (LRU) algorithmis used to determine the content to evict. For example, a k-LRUalgorithm is used, in which the LRU content is evicted in favor ofcontent that has been counted (or samples of which have been counted) atleast k times, where k is an integer. The decision of step 212 thus mayinclude a determination as to whether the k-times threshold has beensatisfied. In other implementations, other eviction algorithms are used.For example, a least-frequently-used (LFU) algorithm is used, in whichthe network device maintains counts for how many times respective cachedcontent items are received or requested and evicts theleast-frequently-used content.

Steps in the method 200 may be combined or broken out and the sequenceof the method 200 may be modified for steps that are notorder-dependent. For example, the order of the steps 206, 208, and/or210 may be varied (e.g., performance of the steps 206, 208, and/or 210may overlap). Also, the decision-making of step 212 and/or caching ofstep 220 may be performed before, during, and/or after the forwarding ofstep 206.

The method 200 thus allows network devices in the network edge to makeintelligent decisions regarding whether or not to cache content. Thenetwork devices are able to balance popularity with the prediction errorfor mobility estimations, such that low latency for providing content isachieved without wasting memory.

FIG. 3 is a block diagram of a network device 300 according to someimplementations. The network device 300 is an example of the networkdevice that performs the method 200 (FIG. 2). For example, the networkdevice may be an RSU 114 or other access point, a gateway 110, or theserver 106. While certain features are illustrated, those of ordinaryskill in the art will appreciate from the present disclosure thatvarious other features have not been illustrated for the sake ofbrevity, and so as not to obscure more pertinent aspects of theimplementations disclosed herein. To that end, in some implementationsthe network device 300 includes one or more processing units (e.g.,CPUs, network processors, etc.) 301, a network interface 302, aprogramming interface 303, memory 304, and one or more communicationbuses 305 for interconnecting these and various other components.

In some implementations, the memory 304 or a non-transitorycomputer-readable storage medium of the memory 304 stores the followingprograms, modules, and data structures, or a subset thereof: an optionaloperating system 306, content-caching module 310,popularity-determination module 314, packet-routing module 316, anddatabase 318. The operating system 306 includes procedures for handlingvarious basic system services and for performing hardware-dependenttasks. The content-caching module 310 may include instructions forcalculating a utility function 312 (e.g., per equations 1, 2, and/or 3).The popularity-determination module 314 may include counters 316 formeasuring the popularity of content items. The content database 318,which caches content items 320, may be an example of memory 108, 112, or116 (FIG. 1). The memory 304 or a non-transitory computer-readablestorage medium of the memory 304 thus may include instructions forperforming the method 200 (FIG. 2).

While various aspects of implementations within the scope of theappended claims are described above, it should be apparent that thevarious features of implementations described above may be embodied in awide variety of forms and that any specific structure and/or functiondescribed above is merely illustrative. Based on the present disclosureone skilled in the art should appreciate that an aspect described hereinmay be implemented independently of any other aspects and that two ormore of these aspects may be combined in various ways. For example, anapparatus may be implemented and/or a method may be practiced using anynumber of the aspects set forth herein. In addition, such an apparatusmay be implemented and/or such a method may be practiced using otherstructure and/or functionality in addition to or other than one or moreof the aspects set forth herein.

It will also be understood that, although the terms “first,” “second,”etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first bias could betermed a second bias, and, similarly, a second bias could be termed afirst bias, without changing the meaning of the description, so long asall occurrences of the first bias are renamed consistently and alloccurrences of the second bias are renamed consistently. The first biasand the second bias are both biases, but they are not the same bias.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the claims. Asused in the description of the embodiments and the appended claims, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

What is claimed is:
 1. A networking method, comprising, at a networkdevice in a network edge, the network device comprising one or moreprocessors and memory storing instructions for execution by the one ormore processors: receiving, from an upstream network device, contentdirected to a mobile device attached to the network edge; making adecision whether to cache the content at the network device based atleast in part on a popularity of the content in a region covered by thenetwork device and a prediction error for an estimated probability thatthe mobile device will transition from the region to another region,wherein the popularity is directly correlated with a first bias towardcaching the content and the prediction error for the estimatedprobability that the mobile device will transition from the region toanother region is inversely correlated with a second bias toward cachingthe content in which a lower prediction error for the estimatedprobability that the mobile device will transition from the region toanother region increases a likelihood toward caching the content; andimplementing the decision, comprising either caching or foregoingcaching the content at the network device in accordance with thedecision such that for the network device serving as a gateway upstreamfrom an access point, the second bias has a magnitude that causes, for agiven prediction error, the network device to more likely decide tocache the content than for the access point to cache the content.
 2. Themethod of claim 1, wherein the decision whether to cache the content atthe network device is not based at least in part on whether othernetwork devices in the network edge have cached the content.
 3. Themethod of claim 2, wherein the network device does not receivecommunications indicating whether the other network devices in thenetwork edge have cached the content.
 4. The method of claim 1, whereinthe decision whether to cache the content at the network device isfurther based at least in part on a quality-of-service (QoS) level ofthe content, wherein the QoS level is directly correlated with a thirdbias toward caching the content.
 5. The method of claim 1, whereinmaking the decision whether to cache the content at the network devicecomprises: calculating a utility function that accounts for the firstbias and the second bias; and determining whether the utility functionsatisfies a threshold.
 6. The method of claim 1, wherein the networkedge comprises a plurality of tiers of network devices, the plurality oftiers of network devices comprising: a first tier of access points; asecond tier of gateways upstream from respective access points of thefirst tier; and a third tier comprising a server upstream fromrespective gateways of the second tier, wherein the network device isselected from the group consisting of the access points of the firsttier, the gateways of the second tier, and the server of the third tier.7. The method of claim 6, wherein: the mobile device is an electronicdevice in a vehicle; the access points of the first tier compriseroad-side units (RSUs); and the server of the third tier is amobile-edge-computing (MEC) server.
 8. The method of claim 6, wherein:for the network device serving as an access point of the first tier andthe mobile device is attached to the access point, the second bias has amagnitude that causes, for a given prediction error, the network deviceto less likely to decide to cache the content than for a respectivegateway upstream of the access point to decide to cache the content. 9.The method of claim 1, further comprising, at the network device,measuring the popularity of the content.
 10. The method of claim 9,wherein measuring the popularity comprises counting at least one ofrequests for the content received by the network device or instances ofthe content received by the network device.
 11. The method of claim 9,wherein measuring the popularity comprises: sampling content-itemrequests or content items received at the network device; and countingsampled content-item requests that are requests for the content orcounting sampled content items that are instances of the content. 12.The method of claim 1, further comprising, at the network device,receiving the estimated probability and the prediction error from anetwork traffic controller, wherein the network traffic controller issituated outside of the network edge.
 13. The method of claim 1, whereinthe prediction error is based at least in part on a presence or anabsence of a historical pattern of movement for the mobile device. 14.The method of claim 1, further comprising, at the network device, beforereceiving the content from the upstream network device: receiving arequest for the content from the mobile device; and forwarding therequest to the upstream network device, wherein the content is receivedin response to the request.
 15. The method of claim 1, furthercomprising: forwarding the content toward the mobile device.
 16. Themethod of claim 15, wherein the receiving and the forwarding are basedon a request from the mobile device for the content.
 17. The method ofclaim 1, wherein the network edge comprises a plurality of tiers ofnetwork devices and the second bias is a function of a tier of thenetwork edge in which the network device is situated.
 18. A networkdevice for deployment in a network edge, comprising: one or moreprocessors; and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor: receiving, from an upstream network device, content directed to amobile device attached to the network edge; making a decision whether tocache the content based at least in part on a popularity of the contentin a region covered by the network device and a prediction error for anestimated probability that the mobile device will transition from theregion to another region, wherein the popularity is directly correlatedwith a first bias toward caching the content and the prediction errorfor the estimated probability that the mobile device will transitionfrom the region to another region is inversely correlated with a secondbias toward caching the content in which a lower prediction error forthe estimated probability that the mobile device will transition fromthe region to another region increases a likelihood toward caching thecontent; and implementing the decision, comprising either caching orforegoing caching the content in accordance with the decision such thatfor the network device serving as a gateway upstream from an accesspoint, the second bias has a magnitude that causes, for a givenprediction error, the network device to more likely decide to cache thecontent than for the access point to cache the content.
 19. The networkdevice of claim 18, wherein the decision whether to cache the content atthe network device is not based at least in part on whether othernetwork devices in the network edge have cached the content.
 20. Anon-transitory computer-readable storage medium storing one or moreprograms configured for execution by a network device in a network edge,the one or more programs comprising instructions for: receiving, from anupstream network device, content directed to a mobile device attached tothe network edge; making a decision whether to cache the content at thenetwork device based at least in part on a popularity of the content ina region covered by the network device and a prediction error for anestimated probability that the mobile device will transition from theregion to another region, wherein the popularity is directly correlatedwith a first bias toward caching the content and the prediction errorfor the estimated probability that the mobile device will transitionfrom the region to another region is inversely correlated with a secondbias toward caching the content in which a lower prediction error forthe estimated probability that the mobile device will transition fromthe region to another region increases a likelihood toward caching thecontent; and implementing the decision, comprising either caching orforegoing caching the content at the network device in accordance withthe decision such that for the network device serving as a gatewayupstream from an access point, the second bias has a magnitude thatcauses, for a given prediction error, the network device to more likelydecide to cache the content than for the access point to cache thecontent.